Algorithmic Tutor-Student Pairing
Introduction
Our company has successfully developed and implemented an intelligent student-tutor matching algorithm for an online tutoring platform.
The algorithm leverages machine learning and data analysis techniques to analyze and interpret students' diverse capabilities and backgrounds seeking tutoring services.
The project aims to optimize the tutoring experience for students and tutors, enhancing learning outcomes and facilitating a seamless and personalized educational journey for the platform's users.
Client Objectives & Requirements
The primary objective of this project is to create a system that effectively processes the information provided by students and tutors. The algorithm matches each student with the most suitable tutor by analyzing factors such as academic proficiency, technical capabilities, nationality, teaching experience, subject expertise, learning preferences, and availability. The ultimate goal is to enhance teaching efficiency and attract more learners using innovative, AI-driven tools.
Current State Analysis
For an online educational firm, it is beneficial if the education service providers can understand the needs of the students in-depth so that they can adjust their content, course materials, lecturing methods, and tutors to give the education to the learners. Not only does it boost the efficiency of the educational experience but it is also a way to attract more new learners and cater to their needs using intelligent tools, now Artificial Intelligence or Machine Learning based.
The most basic systems are recommendation engines that look for the perfect match between the student and the tutors and keep enhancing their recommendations. However, in present days, the systems that are already in place generally lack personalization. Most of them are collaborative filtering-based and rely on what suits best for a mass of people rather than individuals. This is the gap that we have bridged.
Another challenge is a deep understanding of the student's needs. In addition to the subjects, students might have particular preferences about the tutors teaching them, like their educational background, location, method of teaching, etc. This is not found in traditional recommendation models; our model considers this.
Proposed AI Solution
Our proposed AI solution considers various attributes to filter out the most suitable tutors for each student. Key features of the system include:
1. Intelligent Tutor Matching: The system utilizes extensive filtering to analyze student factors, recommending the best tutors based on the priority and importance assigned to each factor. This ensures personalized and effective tutor-student matches.
2. Excel Data Integration: The system seamlessly integrates with Excel files containing student and tutor information, efficiently processing and extracting relevant data for easy access and analysis.
3. Tutor Description Customization: Students can leverage the ChatGPT API to describe their preferred tutor qualities. The generative model then morphs the tutors' descriptions to align with the student's preferences, facilitating a satisfactory match.
Data Strategy
The tutor finder app is deployed on Streamlit, with an Excel file as the backend data source. The system fetches data based on predefined rules, priorities, and filters, considering the student's input. The filtering process identifies the top three tutors that best match the student's requirements and location.
Model Development & Training
The ChatGPT API powers the project's AI segment. Upon receiving the student's input, a custom query and the tutor's original description are sent to the API. The API selects key features and generates a modified description incorporating the student's desired qualities while maintaining the tutor's original bio. The final recommendations include the modified tutor descriptions in both English and Arabic.
Integration With Existing Systems
The system allows administrators to customize the priority and importance levels of various factors used in the matching algorithm. This flexibility enables fine-tuning based on user feedback and evolving educational requirements, ensuring the system remains adaptable and responsive to changing needs.
Performance Metrics
Deployment & Scaling
The tutor finder app is deployed on Streamlit Cloud, which has proven to be a reliable and scalable deployment solution. The system is designed to handle a growing number of users and data, ensuring smooth operations and efficient processing even as the platform expands.
Conclusion
The development of the intelligent student-tutor matching algorithm marks a significant advancement in online tutoring platforms. By addressing the limitations of existing systems and prioritizing personalization, our company has created a solution that profoundly understands and caters to the unique needs of each student.
The integration of advanced AI techniques, such as the ChatGPT API for tutor description customization, further enhances the effectiveness of the matching process. As the platform continues to grow and evolve, this innovative AI approach will undoubtedly contribute to improved learning outcomes and a more engaging, tailored educational experience for all users.
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